Extract spatiotemporal effects from fitted model

Extract posterior distributions of spatiotemporal interaction effects from a fitted tulpa model.

Usage

spatiotemporal_effects(
  object,
  format = c("array", "long", "summary"),
  probs = c(0.025, 0.5, 0.975),
  ...
)

# Default S3 method
spatiotemporal_effects(
  object,
  format = c("array", "long", "summary"),
  probs = c(0.025, 0.5, 0.975),
  ...
)

# S3 method for class 'tulpa_st_summary'
print(x, ...)

Arguments

object

A fit carrying a $spatiotemporal Knorr-Held interaction block (n_spatial, n_times) and its draws in $.internal$spatiotemporal_draws. tulpa() itself has no spatiotemporal = argument; such a block comes from a model package's own fitter (see spatiotemporal() for what the engine itself fits), and the default method reads any fit, of any class, that stores it this way.

format

Output format: "array" (default, S x T x draws), "long" (data frame with s, t, draw, value columns), or "summary" (posterior summaries).

probs

Quantiles to compute if format = "summary".

...

Ignored

x

A tulpa_st_summary table.

Value

For format = "array", an S x T x draws numeric array carrying n_spatial, n_times and n_draws attributes; for "long", a data frame with columns s, t, draw and value; for "summary", a tulpa_st_summary data frame with columns s, t, mean, one column per entry of probs and sd, which has print() and plot() methods.

Examples

# A Knorr-Held interaction block is attached by a model package's own
# fitter (see spatiotemporal() for what the engine fits). The accessor reads
# its layout and an n_draws x (S * T) draw matrix stored s-fastest: column
# (t - 1) * S + s holds region s at time t. Here both are simulated for
# S = 3 regions over T = 4 times.
set.seed(1)
S <- 3L; n_t <- 4L
st_mean <- outer(c(-0.5, 0, 0.5), seq(-0.3, 0.3, length.out = n_t))
draws <- matrix(rnorm(100 * S * n_t, mean = rep(c(st_mean), each = 100),
                      sd = 0.1), nrow = 100)
fit <- structure(
  list(spatiotemporal = list(n_spatial = S, n_times = n_t),
       .internal = list(spatiotemporal_draws = draws)),
  class = "tulpa_fit")

st_effects <- spatiotemporal_effects(fit, format = "summary")
head(st_effects)
dim(spatiotemporal_effects(fit, format = "array"))